On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization

Undral Byambadalai, Tomu Hirata, Tatsushi Oka, Shota Yasui
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:6102-6125, 2025.

Abstract

This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron’s biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and analyses of real-world datasets highlight the practical advantages of our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-byambadalai25a, title = {On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization}, author = {Byambadalai, Undral and Hirata, Tomu and Oka, Tatsushi and Yasui, Shota}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {6102--6125}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/byambadalai25a/byambadalai25a.pdf}, url = {https://proceedings.mlr.press/v267/byambadalai25a.html}, abstract = {This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron’s biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and analyses of real-world datasets highlight the practical advantages of our method.} }
Endnote
%0 Conference Paper %T On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization %A Undral Byambadalai %A Tomu Hirata %A Tatsushi Oka %A Shota Yasui %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-byambadalai25a %I PMLR %P 6102--6125 %U https://proceedings.mlr.press/v267/byambadalai25a.html %V 267 %X This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron’s biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and analyses of real-world datasets highlight the practical advantages of our method.
APA
Byambadalai, U., Hirata, T., Oka, T. & Yasui, S.. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:6102-6125 Available from https://proceedings.mlr.press/v267/byambadalai25a.html.

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